End-to-End Autoencoder for Drill String Acoustic Communications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910437039144960 |
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| author | Lezhenin, Iurii Sidnev, Aleksandr Tsygan, Vladimir Malyshev, Igor |
| author_facet | Lezhenin, Iurii Sidnev, Aleksandr Tsygan, Vladimir Malyshev, Igor |
| contents | Drill string communications are important for drilling efficiency and safety. The design of a low latency drill string communication system with high throughput and reliability remains an open challenge. In this paper a deep learning autoencoder (AE) based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acousticdrill string communications. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM system in terms of BER and PAPR, operating with lower latency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | End-to-End Autoencoder for Drill String Acoustic Communications Lezhenin, Iurii Sidnev, Aleksandr Tsygan, Vladimir Malyshev, Igor Machine Learning Signal Processing Drill string communications are important for drilling efficiency and safety. The design of a low latency drill string communication system with high throughput and reliability remains an open challenge. In this paper a deep learning autoencoder (AE) based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acousticdrill string communications. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM system in terms of BER and PAPR, operating with lower latency. |
| title | End-to-End Autoencoder for Drill String Acoustic Communications |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2405.03840 |